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Found 23 papers, 3 papers with code
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow.
Sample Complexity of Kernel-Based Q-Learning
To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model.
Delayed Feedback in Kernel Bandits
An abstraction of the problem can be formulated as a kernel based bandit problem (also known as Bayesian optimisation), where a learner aims at optimising a kernelized function through sequential noisy observations.
Collaborative Learning in Kernel-based Bandits for Distributed Users
We study collaborative learning among distributed clients facilitated by a central server.
Provably and Practically Efficient Neural Contextual Bandits
The non-asymptotic error bounds may be of broader interest as a tool to establish the relation between the smoothness of the activation functions in neural contextual bandits and the smoothness of the kernels in kernel bandits.
Near-Optimal Collaborative Learning in Bandits
In this paper, we provide new lower bounds on the sample complexity of pure exploration and on the regret.
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization.
Open Problem: Tight Online Confidence Intervals for RKHS Elements
Confidence intervals are a crucial building block in the analysis of various online learning problems.
Uniform Generalization Bounds for Overparameterized Neural Networks
As a byproduct of our results, we show the equivalence between the RKHS corresponding to the NT kernel and its counterpart corresponding to the Mat\'ern family of kernels, showing the NT kernels induce a very general class of models.
Optimal Order Simple Regret for Gaussian Process Bandits
Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$.
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space.
On Information Gain and Regret Bounds in Gaussian Process Bandits
For the Mat\'ern family of kernels, where the lower bounds on $\gamma_T$, and regret under the frequentist setting, are known, our results close a huge polynomial in $T$ gap between the upper and lower bounds (up to logarithmic in $T$ factors).
Scalable Thompson Sampling using Sparse Gaussian Process Models
We provide theoretical guarantees and show that the drastic reduction in computational complexity of scalable TS can be enjoyed without loss in the regret performance over the standard TS.
Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization
A framework based on iterative coordinate minimization (CM) is developed for stochastic convex optimization.
Amortized variance reduction for doubly stochastic objectives
Approximate inference in complex probabilistic models such as deep Gaussian processes requires the optimisation of doubly stochastic objective functions.
Regret Bounds for Noise-Free Kernel-Based Bandits
Kernel-based bandit is an extensively studied black-box optimization problem, in which the objective function is assumed to live in a known reproducing kernel Hilbert space.
Ordinal Bayesian Optimisation
Bayesian optimisation is a powerful tool to solve expensive black-box problems, but fails when the stationary assumption made on the objective function is strongly violated, which is the case in particular for ill-conditioned or discontinuous objectives.
Adaptive Sensor Placement for Continuous Spaces
We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events.
Stochastic Gradient Descent on a Tree: an Adaptive and Robust Approach to Stochastic Convex Optimization
Online minimization of an unknown convex function over the interval $[0, 1]$ is considered under first-order stochastic bandit feedback, which returns a random realization of the gradient of the function at each query point.
Decision Variance in Online Learning
In this paper, a risk-averse online learning problem under the performance measure of the mean-variance of the rewards is studied.
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs
Two settings of complete and partial side information based on whether the UIG is fully revealed are studied and a general two-step learning structure consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is proposed to fully exploit the topological structure of the side information.
Anomaly Detection in Hierarchical Data Streams under Unknown Models
We consider the problem of detecting a few targets among a large number of hierarchical data streams.
Risk-Averse Multi-Armed Bandit Problems under Mean-Variance Measure
We show that the model-specific regret and the model-independent regret in terms of the mean-variance of the reward process are lower bounded by $\Omega(\log T)$ and $\Omega(T^{2/3})$, respectively.
